Pandas Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). The axis labels are collectively called index. Pandas Series is nothing but a column in an excel sheet. In this article, we will see various ways of creating a series using different data types.
Creating Series from list
The list of some values form the series of that values uses list index as series index.
Python
# import pandas as pd import pandas as pd # simple list lst = ['G','E','E','K','S','F', 'O','R','G','E','E','K','S'] # forming series s = pd.Series(lst) # output print(s) |
Output :
0 G 1 E 2 E 3 K 4 S 5 F 6 O 7 R 8 G 9 E 10 E 11 K 12 S dtype: object
Creating Series from dictionary
Dictionary of some key and value pair for the series of values taking keys as index of series.
Python3
# import pandas as pd import pandas as pd # simple dict dct = {'G':2,'E':4,'K':2,'S':2, 'F':1,'O':1,'R':1} # forming series s = pd.Series(dct) # output print(s) |
Output :
G 2 E 4 K 2 S 2 F 1 O 1 R 1 dtype: int64
Creating Series from Numpy array
The 1-D Numpy array of some values form the series of that values uses array index as series index.
Python3
# import pandas as pd import pandas as pd # import numpy as np import numpy as np # numpy array arr = np.array(['G','E','E','K','S','F', 'O','R','G','E','E','K','S']) # forming series s = pd.Series(arr) # output print(s) |
Output :
0 G 1 E 2 E 3 K 4 S 5 F 6 O 7 R 8 G 9 E 10 E 11 K 12 S dtype: object
